A prior art data mining and statistical scoring system 100 is shown in FIG. 1. In typical systems a data mining system 102 mines a database of historical data to create a model 103 of a particular behavior or result that can be derived from the historical data. For example, data mining in historical data 101 may determine from personal data and known purchasing history that a certain demographic may be particularly likely to purchase a particular product or type of product. In another example, historical data regarding individual demographics and driving histories may yield insights into insurance risks for drivers.
Once a particular model 103 has been constructed from the historical data, the model can be applied to new data to make predictions about unseen behavior. The process of using a model to make predictions about future events or behavior is called “scoring,” and the output of the prediction is referred to as a score. While scores can take any form, most scores are represented as a number, such as a score between 0 and 1 that predicts the likelihood of a future event. The engine that applies model 103 to new data is referred to as a scoring engine, such as scoring engine 105. Scoring engine 105 uses existing data 104 and model 103 to make the prediction, or score, 106.
While there are many general-purpose statistical analysis systems that provide for the aggregation of data by some user-defined criteria and from some user-defined source, none of the existing systems are able to process raw data from randomly reporting remote devices or to score data from these sources based on a user-defined model. The existing systems provide general approaches for handling statistics or population scoring, but do not address unique issues arising from remote or raw/unprocessed device data.